基于EEM-PARAFAC结合微生物追踪模型及其对复杂污染区域的影响的来源分配,中国武区
Yuanjun Peng1, Lili Liu2, Xu Wang2
1School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Environmental pollution (Barking, Essex : 1987)
|February 18, 2024
概括
在复杂的环境中识别污染源是一项挑战. 微生物可追溯模型,如FEAST和SourceTracker,有效地确定污染来源,优于精确水质管理的传统方法.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 微生物生态学 微生物生态学
背景情况:
- 改善地表水质量变得越来越困难,尤其是在工业和农业污染混合的地区.
- 有效的污染源分配对于在复杂环境中制定有针对性的控制措施至关重要.
研究的目的:
- 开发和评估用于使用三维光和微生物可追溯性的污染源分配的技术框架.
- 在复杂的工业和农业污染场景中比较不同来源分配模型的有效性.
主要方法:
- 开发了一个整合三维光刺激-发射矩阵 (EEM) 分析和微生物源追踪的框架.
- 应用主要组件分析-绝对主要组件得分-多重线性回归 (PCA-APCS-MLR) 用于初始源识别.
- 使用的微生物可追溯性模型:微生物源追踪 (FEAST) 和SourceTracker的快速预期最大化.
主要成果:
- PCA-APCS-MLR在区分工业来源方面存在局限性,导致高比例的未知来源 (平均39.60%).
- 微生物可追溯性模型准确地确定了7个污染源和自然污染源,大大减少了未知来源的贡献 (FEAST:19.81%,SourceTracker:16.72%).
- 与SourceTracker相比,FEAST表现出更高的灵敏度和更快的计算速度,用于污染源的分配.
结论:
- 与PCA-APCS-MLR相比,微生物可追溯性模型在复杂环境中提供了更高的准确性和污染源识别能力.
- 由于其效率和准确性,FEAST是指导精确区域污染控制的有希望的工具.
- 精确的来源分配是混合用途景观中有效地管理地表水质量的关键.
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